Photovoltaic Cell Defect Detection Based on Weakly Supervised Learning With Module-Level Annotations
Hyungu Kang, Jeong‐Min Hong, Jiwon Lee, Seokho Kang
Abstract
Recently, convolutional neural networks (CNNs) have proven successful in automating the detection of defective photovoltaic (PV) cells within PV modules. Existing studies have built a CNN based on fully supervised learning, which requires a training dataset consisting of PV cell images annotated according to whether the individual cells are defective. However, manually annotating all the PV cells is labor-intensive and time-consuming, leading to substantial annotation costs. In this study, we propose a weakly supervised learning method to build a CNN for cell-level defect detection in a cost-efficient manner. Our method uses a training dataset solely with module-level annotations indicating whether each PV module contains defective cells, thereby substantially reducing the required annotation costs. The CNN is trained in a weakly supervised manner such that all cells in a normal module are classified as normal and at least one cell in a defective module is classified as defective. The CNN can then be used to detect cell-level defects in new PV modules. The effectiveness of the proposed method is validated through experiments using real-world data provided by a PV module manufacturer.